ChenFang2019BetaRankTest {intrinsicFRP}

Asset Pricing Model Identification via Chen-Fang (2019) Beta Rank Test

Description

Tests the null hypothesis of reduced rank in the matrix of regression loadings for test asset excess returns on risk factors using the Chen-Fang (2019) doi:10.3982/QE1139 beta rank test. The test applies the Kleibergen-Paap (2006) doi:10.1016/j.jeconom.2005.02.011 iterative rank test for initial rank estimation when target_level_kp2006_rank_test > 0, with an adjustment to level = target_level_kp2006_rank_test / n_factors. When target_level_kp2006_rank_test <= 0, the number of singular values above n_observations^(-1/4) is used instead. It presumes that the number of factors is less than the number of returns (n_factors < n_returns). All the details can be found in Chen-Fang (2019) doi:10.3982/QE1139.

Usage

ChenFang2019BetaRankTest(
  returns,
  factors,
  n_bootstrap = 500,
  target_level_kp2006_rank_test = 0.05,
  check_arguments = TRUE
)

Arguments

returns

Matrix of test asset excess returns with dimensions ⁠n_observations x n_returns⁠.

factors

Matrix of risk factors with dimensions ⁠n_observations x n_factors⁠.

n_bootstrap

The number of bootstrap samples to use in the Chen-Fang (2019) test. Defaults to 500 if not specified.

target_level_kp2006_rank_test

The significance level for the Kleibergen-Paap (2006) rank test used for initial rank estimation. If set above 0, it indicates the level for this estimation within the Chen-Fang (2019) rank test. If set at 0 or negative, the initial rank estimator defaults to the count of singular values exceeding n_observations^(-1/4). The default value is 0.05 to account for multiple testing.

check_arguments

Logical flag to determine if input arguments should be checked for validity. Default is TRUE.

Value

A list containing the Chen-Fang (2019) rank statistic and the associated p-value.

Examples

# import package data on 6 risk factors and 42 test asset excess returns
factors = intrinsicFRP::factors[,-1]
returns = intrinsicFRP::returns[,-1]

# compute the model identification test
hj_test = ChenFang2019BetaRankTest(returns, factors)